Abstract: A method and apparatus using machine learning for scheduling server maintenance. In one embodiment of the method, load values for a server are recorded over a period of time, wherein each of the load values is time stamped with a date and time. A first plurality of the load values are classified. The classified first plurality of values are then processed to create a model for predicting a load value of the server. The model is used to generate a first predicted load value of the server for a first date and a first time.

Abstract: Methods, information handling systems and computer readable media are disclosed for detection and repair of fragmentation in databases. In one embodiment, a method includes obtaining log data reflecting transactions in a database, where the log data is generated during operation of the database. The method continues with applying a machine learning classification model to at least a portion of the log data to obtain a first prediction, where the first prediction indicates whether defragmentation of the database should be scheduled. In this embodiment the method also includes using a machine learning time series forecasting model to obtain a second prediction, where the second prediction identifies a future time interval of low relative database utilization, and scheduling a defragmentation procedure for performance during the future time interval of low relative database utilization.

Abstract: A method and apparatus using machine learning for scheduling server maintenance. In one embodiment of the method, load values for a server are recorded over a period of time, wherein each of the load values is time stamped with a date and time. A first plurality of the load values are classified. The classified first plurality of values are then processed to create a model for predicting a load value of the server. The model is used to generate a first predicted load value of the server for a first date and a first time.

Abstract: Methods, information handling systems and computer readable media are disclosed for detection and repair of fragmentation in databases. In one embodiment, a method includes obtaining log data reflecting transactions in a database, where the log data is generated during operation of the database. The method continues with applying a machine learning classification model to at least a portion of the log data to obtain a first prediction, where the first prediction indicates whether defragmentation of the database should be scheduled. In this embodiment the method also includes using a machine learning time series forecasting model to obtain a second prediction, where the second prediction identifies a future time interval of low relative database utilization, and scheduling a defragmentation procedure for performance during the future time interval of low relative database utilization.